Related work on robustness of interpretations. Although explanations have enabled deeper understanding of DNNs, there are concerns regarding their robustness. [1]} showed that explanations can easily be misled by introducing imperceptible noise in the input image. Several other works have highlighted similar problems on vision, natural language and reinforcement learning such as [2]}, [3]}, [4]}, [5]}, [6]}, [7]}. Similarly, concept explanation methods are also fragile to small perturbations to input samples [8]}. Such concerns regarding fragility of model explanations have prompted related research in improving robustness of explanation methods. For example, [9]}, [10]}, [11]} proposed learning more robust feature attributions while [12]}, [13]}, [14]} try to learn more robust concepts. However, existing defense methods [9]}, [10]}, [11]} on improving the robustness of the feature-level model explanations cannot be directly adopted here. The reason is that they only focus on the post-hoc interpretations, while we work on the intrinsic concept-based interpretable network.
